Marker assisted selection in plant breeding

Abstract

Marker assisted selection (MAS) is 'smart breeding' or fast track plant breeding technology. It is one tool utilized in breeding companies and research institutes for fast development of improved varieties, giving possibility to select desirable traits more directly using DNA markers. In this review, we discussed the use of MAS in biotic, abiotic, quality and other agronomic traits. Besides, we emphasized the importance of MAS at ICARDA and underlined the successful application of MAS in the last 10 years. The use of molecular markers makes the process of selecting parental lines more efficient based on genetic diversity analysis. It can aid the conventional breeding, especially for certain biotic and abiotic traits laborious to manage. Still, MAS contributed very little to the release of improved cultivars with greater tolerance to abiotic stresses, with only a few exceptions. MAS was extensively used to improve rice varieties, mainly resistant to bacterial blight and blast disease and was applied in drought tolerance along with GPC (Grain protein content) in quality traits. MAS at ICARDA is used to characterize new parental materials for disease resistance genes as well as in screening advanced lines with a focus on association mapping and identification of new QTLs. The application of MAS increased in the last decade. It is more and more used in different crops. However, rice is still the dominant crop in terms of number of publications using MAS.

Full text

237
Mor. J. Agri. Sci. 1(5): 237-247, September 2020
Marker assisted selection in plant breeding
Fatima HENKRAR1*, Sripada UDUPA1
1 International Center for Agricul-
tural Research in the Dry Areas
(ICARDA), Rabat, Morocco
* Corresponding author
f.henkrar@cgiar.org
Received 10/07/2020
Accepted 18/07/2020
Abstract
Marker assisted selection (MAS) is ‘smart breeding’ or fast track plant breeding technol-
ogy. It is one tool utilized in breeding companies and research institutes for fast devel-
opment of improved varieties, giving possibility to select desirable traits more directly
using DNA markers. In this review, we discussed the use of MAS in biotic, abiotic,
quality and other agronomic traits. Besides, we emphasized the importance of MAS
at ICARDA and underlined the successful application of MAS in the last 10 years. e
use of molecular markers makes the process of selecting parental lines more ecient
based on genetic diversity analysis. It can aid the conventional breeding, especially for
certain biotic and abiotic traits laborious to manage. Still, MAS contributed very little
to the release of improved cultivars with greater tolerance to abiotic stresses, with only
a few exceptions. MAS was extensively used to improve rice varieties, mainly resistant
to bacterial blight and blast disease and was applied in drought tolerance along with
GPC (Grain protein content) in quality traits. MAS at ICARDA is used to characterize
new parental materials for disease resistance genes as well as in screening advanced
lines with a focus on association mapping and identication of new QTLs. e applica-
tion of MAS increased in the last decade. It is more and more used in dierent crops.
However, rice is still the dominant crop in terms of number of publications using MAS.
Keywords: marker assisted selection, plant, biotic stress, abiotic stress, quality, ICARDA
INTRODUCTION
Wheat breeders continuously seek for new techniques
which can be used for assembling target traits into
new wheat cultivars and achieve the same breeding
progress in a much shorter time than through conven-
tional breeding. e main goals of wheat breeding are
increasing the yield, improving the resistance to abiotic
and biotic stresses, improving the quality. While simple
traits can easily be detected, other complex traits such as
disease resistance or drought tolerance are much more
dicult to determine for the breeder. Young (1999)
wrote: “Before the advent of DNA marker technology,
the idea of rapidly uncovering the loci controlling com-
plex, multigenic traits seemed like a dream’’. Now with
DNA marker technology, this dream became reality.
e capacity of DNA markers to detect allelic variation
in the genes underlying traits oers a great promise
for plant breeding. By using DNA markers to assist in
plant breeding, eciency and precision could be greatly
increased. e use of DNA markers in plant breeding is
called marker-assisted selection (MAS).
Denition of MAS
Marker assisted selection (MAS) is ‘smart breeding’ or
fast track plant breeding technology. It is one tool utilized
in breeding companies and research Institutes for fast
development of improved varieties, giving possibility to
select desirable traits more directly using DNA mark-
ers. e molecular markers can then be used to assist
breeders track whether the specic gene or chromosome
segment(s) known to aect the phenotype of interest is
present in the individuals or populations of interest. e
potential of MAS, thus, moving from phenotype based
towards genotype based selection using markers linked
to gene of interest. anks to the advent of DNA mark-
ers in the late of 1970s, it has now become possible to
directly target genomic regions that are involved in the
expression of traits of interest.
e history of Marker assisted selection
e idea of MAS begins with the theory of quantitative
trait loci (QTLs) mapping described by sax (1923), when
he observed an association between monogenic trait
(Seed coat pigmentation) and polygenic trait (seed size).
is concept was further elaborated by oday (1961),
who suggested mapping and characterizing all QTLs
involved in complex traits using single gene marker.
e rst DNA-based genetic markers were restriction
fragment length polymorphisms, RFLPs (Botstein et
al., 1980). Permit to construct the rst map for tomato
using 57 RFLPs in 1986 (Bernatzky and Tanksley, 1986).
Beckmann and Soller (1986) described the rst use
of restriction fragment length polymorphism (RFLP)
markers in crop improvement including theoretical is-
sues related to marker-assisted backcrossing (MABC)
for improvement of qualitative traits. Tanksley et al.
(1989) published the use of RLFP as tool to select desir-
© Moroccan Journal of Agricultural Sciences • e-ISSN: 2550-553X www.techagro.org
Review

238 Henkrar and Udupa: Marker assisted selection for plant breeding
able lines. He reported the possibility to analyzing plants
at the seedling stage, screening multiple characters that
would normally be epistatic with one another, mini-
mizing linkage drag, and rapidly recovering a recurrent
parent’s genotype. At that time, the idea of selection of
target genes based on genotypes rather than phenotype
was extremely attractive to plants breeders (Young,
1999). All those initiatives open the door to marker
technology and development of simpler DNA marker
involving PCR techniques such as Random-Amplied
Polymorphic DNAs, RAPDs (Williams et al., 1990), Am-
plied Fragment Length Polymorphisms, AFLPs (Vos
et al., 1995), Simple Sequence Repeat, SSR also known
microsatellites (Powell et al., 1996) and Single Nucleo-
tide Polymorphisms, SNPs (Gupta et al., 2001). Along
with, the research boost in DNA marker technology
and produce specics markers like Sequence Character-
ized Amplied Region, SCAR (Paran and Michelmore,
1993), Cleaved Amplied Polymorphic Sequence, CAPS
(Maeda et al., 1990), Sequence Tagged Site, STS (Olsen
et al., 1989), Expressed Sequence Tags, EST (Jongeneel,
2000), and most recent marker Diversity Arrays Tech-
nology, DArT (Jaccoud et al., 2001).
e application of molecular marker in paren-
tal selection and predicting heterosis
e plant breeders seek ways of facilitating the use of
available germoplasm eectively for plant improvement.
One hand, the use of molecular markers makes the
process of selecting parental lines more ecient. Based
on genetic diversity calculated from ngerprinting data,
plant material can be classied into genetic pools. is
information can be extremely helpful for identifying
the most appropriate parental lines to be crossed. Lom-
bardi et al. (2014) reported that a selection of divergent
parental genotypes for breeding should be made active
on the basis of systematic assessment of genetic distance
between genotypes, rather than passively based on
geographical distance. In other hand, classify parental
lines into heterotic groups for the creation of predict-
able hybrids (Acquaah, 2012). e concept of heterotic
groups was developed by Maize research using RFLP-
based genetic distances of inbreds for the prediction of
hybrid performance and heterosis of single crosses in
maize has given dierent results (Melchinger, 1993). e
genetic distance estimates based on molecular marker
estimates have been eective in grouping related germ-
plasm (Melchinger et al., 1998). Martin et al. (1995)
used both pedigree records and Sequence Tagged Sites
(STS) molecular markers to determine the relationship
between genetic diversity and agronomic performance
of the hybrids and they found signicant associations
between genetic distance based on pedigree and kernel
weight and protein concentration of the heterosis. Zhao
et al. (2008) and others also suggested that genetic dis-
tances revealed by molecular markers were highly and
positively correlated with heterosis in rice. However, the
relationship between parents and genotypic variance
components in their progenies has been reported as
weak or non-signicant across many studies (Helms et
al., 1997; Burkhamer et al., 1998; Melchinger et al., 1998;
Bohn et al., 1999; Gumber et al., 1999; Brachi et al., 2010;
Hung et al., 2012).
MAS in disease resistance breeding
Plant diseases are the result of infection by other organ-
isms that adversely aect the growth, physiological func-
tioning and productivity of a plant. Plant diseases can
drastically aect a country’s economy. erefore, disease
management has always been one of the main objectives
of any crop improvement program. ere are at least
50000 diseases of economic plants and new diseases are
discovered every year (Lucas, 1992). Plant diseases are
sometimes grouped according to the symptoms they
cause (root rots, wilts, leaf spots, blights, rusts, smuts),
to the plant organ they aect (root diseases, stem dis-
eases, foliage diseases), or to the types of plants aected
(eld crop diseases, vegetable diseases, turf diseases,
etc.) (Agrios, 2004). Using plant resistance genes for
developing disease-resistant varieties are a convenient
alternative to other measures like pesticides or other
chemical control methods employed to protect crops
from diseases (Gururani et al., 2012). at is the objective
of plant breeding, the identication of resistant plants,
which are then crossed with agronomically acceptable
but susceptible plants. A program of backcrossing to the
susceptible parent and selection of resistant phenotypes
leads to the production of plants that are similar to the
susceptible parent but having the required resistance.
Breeders have successfully developed lines resistant to
diseases by integrating R-genes into their cultivars. How-
ever, it is not always the case due to the time-consuming
by conventional breeding process that take around 10
years, and by this time, in some instances, the pathogen
has already evolved a variant that is not recognized by
the improved cultivar, leading to susceptibility. DNA
markers have enormous potential to improve the ef-
ciency and precision of conventional plant breeding
via marker-assisted selection (MAS) by reducing the
reliance on laborious and fallible screening procedures.
Especially for durable resistance or no specic, that be-
comes a challenge and the best way to overcome the new
races pathogen evolution. e use of molecular markers
in selection can aid the conventional breeding, especially
for certain traits laborious to manage it. Xu and Crouch
(2008) specify four kinds of traits which DNA markers
should be helpful. (i) traits that are dicult to manage
through conventional phenotypic selection because they
are expensive or time-consuming to measure, have low
penetrance or complex inheritance; (ii) traits whose
selection depends on specic environments or host de-
velopmental stages; (iii) maintenance of recessive alleles
during backcrossing or for speeding up backcross breed-
ing in general; and (iv) pyramiding multiple monogenic
traits or several QTL for a single disease resistance with
complex inheritance. Several studies reported the ap-
plication of molecular markers as a tool to assist pheno-
typic method to improve concerned traits. For example,
Miklas et al., (2006) reported in bean that the most
eective strategy to improve bean host plant resistance

239
Mor. J. Agri. Sci. 1(5): 237-247, September 2020
to common bacterial blight was a combination of MAS
with periodic phenotypic selection, because it allows the
retention of minor QTL and selects epistatic interactions
that contribute to improved disease resistance. Wilde et
al. (2008) noted the eciency of MAS with phenotypic
selection combination in improving resistance against
Fusarium head blight. One of the successful applications
of MAS in breeding disease resistance was in Indonisia,
and the release of two rice varieties ‘Angke’ and ‘Conde’,
which are resistant to bacterial leaf blight infection
(Bustamam et al., 2002). Also, Zhao et al. (2012) succeed
in introgression of qHSR1, which is a QTL related to
head smut in head smut–susceptible lines via marker-
assisted selection, which has signicantly reduce disease
incidence over time in maize.
MAS in abiotic stress breeding
Abiotic stress is dened as environmental conditions
that reduce growth and yield below optimum levels.
Plant responses to abiotic stresses are dynamic and
extremely complex (Cramer, 2010; reviewed by Cramer
et al., 2011). Boyer (1982) indicated that environmental
factors may limit crop production by as much as 70%.
Many genes aect stress tolerance, but few of the identi-
ed genes have proven useful in the eld. e genom-
ics era has allowed dissection of the physiological and
molecular traits underlying stress tolerance mechanisms
to an unprecedented level. Integrated omics analyses
have markedly increased our understanding of plant re-
sponses to various stresses. ese analyses are important
for comprehensive analyses of abiotic stress responses,
especially the nal steps of stress signal transduction
pathways (Cramer et al., 2011). e application of
omics technologies has contributed to the development
of stress-tolerant crops in the eld. Several genes are
identied to have a great role in abiotic stress tolerance.
For instance, SNACs were characterized as factors that
regulate expression of genes important for drought and
salinity tolerance in rice (Hu et al., 2006; reviewed by
Todaka et al., 2012). DREB1/CBF regulon involved in
cold-stress-responsive gene expression, and DREB2
involved in osmotic-stress-responsive gene expression
(Yamaguchi-Shinozaki and Shinozaki, 2006). e re-
views of Nakashima et al. (2009) and Todaka et al. (2012)
discussed more about dierent abiotic stress genes iden-
tied in transcriptomic analyses. is comprehensive
knowledge about the genes involved in stress response
and tolerance will further allow a more precise use of
MAS and transgenics (Dita et al., 2006). However, still
MAS contributed very little to the release of improved
cultivars with greater tolerance to abiotic stresses, with
only a few exceptions (LeDeaux et al., 2006; MacMillan
et al., 2006; Ribaut and Ragot, 2007; Welcker et al., 2007).
e marker assisted selection was applied especially in
drought tolerance. For instance, Courtois et al. (2003)
used MAS to transfer a number of QTLs related to a
deep rooted character from the japonica upland cultivar
‘‘Azucena’’ to the lowland indica variety ‘‘IR64’’. MAS se-
lected lines showed a greater root mass and higher yield
in drought stress. Steele et al. (2004) made novel method
termed Marker-evaluation selection in rice crop. is ap-
proach used a very large segregating population derived
from a wide cross between the upland variety Kalinga
III and the irrigated variety IR64. e population was
selected for overall agronomic performance in several
target stress environments over many generations and
the products from the selection were evaluated with
markers. Varieties developed through MABC (e.g. Asho-
ka 228) have better drought resistance as they yield more
than parent Kalinga III. Similarly, Steele et al. (2006)
used marker assisted breeding program to improve some
root traits related to drought tolerance in an Indian rice
cultivar Kalinga III. ey introgressed ve QTL regions
associated with root traits from Azucena into Kalinga
III. e target QTL on chromosome 9 (RM242-RM201)
signicantly increased root lengths under drought stress.
MAS in improving agronomic and seed quality
traits
Development of cultivars with high agronomic perfor-
mance and good quality is preeminent in crop breeding
programs. Several agronomic and quality traits are poly-
genic trait controlled by many QTL/genes with smaller
eects, such as yield and GPC, seed size seed oil content,
days to ower and to maturity, ber length and strength,
etc.; or by few QTL/genes with major eects such as ker-
nel color, ower color, stem color, etc. ose traits cannot
be found through phenotypic evaluation alone because
they are highly sensitive to environmental changes. In
addition, it is dicult to produce ideal cultivars with high
yield and good quality due to the existing negative corre-
lation between those traits (Barnard et al., 2002; Chung et
al., 2003; Yagdi and Sozen, 2009; Sourour et al., 2018; Ma
et al., 2012). erefore, Molecular detection and genetic
tracking of quantitative trait loci (QTL) for agronomic
and quality traits will aect positively in manipulation
of those traits, and will increase the accuracy of selec-
tion. Hence, the identication of QTLs related to quality
and agronomic traits is important as an entry point for
marker assisted selection. Nowadays, the studies are fo-
cusing on desiccation of stable QTLs responsible for ag-
ronomic and quality traits in major crops using genome
wide association mapping (GWAS), linkage mapping
and single nucleotide polymorphism (SNPs). Chen et al.
(2016) identies useful QTL qGW4.05 related to Kernel
weight and kernel size in Maize. e agronomic and
quality traits of Brassica napus has been dissected using
Genome wide association mapping and using a 6K single
nucleotide polymorphism (SNP) array (Körber et al.,
2016). New QTLs associated with protein and oil content
were identied (Cao et al., 2017; Karikari et al., 2019).
e MAS was extensively used for improving GPC. e
selection and introgression of a high GPC allele of Gpc-
B1 has been achieved in several of the released wheat
cultivars (DePauw et al., 2005; Humphreys et al., 2010;
Randhawa et al., 2013) using molecular markers. A suc-
cessful example of an integrated approach of combining
phenotypic selection with marker assisted backcross
breeding in wheat for introgression of Gpc-B1 in Indian

240 Henkrar and Udupa: Marker assisted selection for plant breeding
wheat cultivar HUW468 (Vishwakarma et al., 2016).
MAS was adopted for studying the genome composition
of winter cultivars Zhengmai 7698 using closely linked
or functional markers for gluten protein quality, grain
hardness and our color (Li et al., 2018). It was used to
improve oil content in sunower, and the Marker F4-
R1 was validated and proved to be the most ecient in
detecting high oil content in sunower (Dimitrijević et
al., 2017). Besides, e MAS was frequently used in the
most important trait, yield. Liang et al. (2004) developed
a new stable improved line ‘9311xOryza rupogon’
with yield-enhancing genes and high yield potential
using SSRs tightly linked markers. Kumar et al. (2018)
combined grain yield and genotypic data from dierent
generations (F3 to F8) for ve marker-assisted breeding
programs for analyzing the eectiveness of synergistic ef-
fect of phenotyping and genotyping in early generations.
ey found genotyping and phenotyping cost savings of
25–68% compared with the traditional marker-assisted
selection approach.
Marker assisted selection at ICARDA
Crop improvement at ICARDA aims to conserve agricul-
tural biodiversity in dry areas and to use these resources
to improve food crops through breeding. It covers durum
and bread wheat, barley, chickpea, lentil, faba bean, gras-
spea, and forage and pasture crops. ICARDA’s approach
combines conventional and biotechnology research to
identify molecular markers and to use it. Identication
and utilization of molecular markers for marker assisted
selection would enhance the development of widely
adapted and high yielding varieties with resistance/toler-
ance to abiotic and biotic resistance and acceptable level
of end use quality. e benet of this ‘marker-assisted
selection’ is that it will make the breeding process faster
and more precise. As a result, breeders and farmers will
see rapid improvements in crop production, enabling
them to improve livelihoods and boost food security.
MAS at ICARDA is used to characterize new parental
materials for disease resistance genes (stripe rust, leaf
rust, stem rust, nematodes); insect resistance (Hessian y
and Russian Wheat Aphid), phonological traits such as
photoperiodism (Ppd), vernalization requirement (Vrn);
plant height (Rht), grain hardness and other desirable
genes (Tadesse et al., 2012 and 2016). Molecular markers
are also used for pyramiding dierent resistance genes
and developing multi-line cultivars targeting for durable
resistance to the disease. It helps of screening real hybrids
F1, F2, BC1F1 populations. e use of molecular markers
and MAS started at ICARDA since long, by identifying
and mapping gene resistance to lentil, pea and chickpea
pathogen (Baum et al., 2000). e use of molecular
techniques and biotechnology tools have expanded
considerably, the techniques are applied to almost all
crops and concentrated on the development of marker-
assisted selection and characterization and identica-
tion of fungal pathogens and nematodes. ICARDA has
focused on the propagation of the molecular techniques
and their application in crop improvement by organiz-
ing extensive training to young researchers, students,
junior level scientists, and also technicians (Ryan et al.,
2012). CIMMYT, Biodiversity, International Centre for
Agricultural Research in the Dry Areas (ICARDA), and
IRRI have partnered with national research organizations
from 13 countries in Africa and South Asia to co-generate
and share technologies for genetic characterization and
marker-assisted improvement of wheat, barley, and rice,
focusing on traits and alleles that are important for the
crops adaptation to climatic changes (Halewood et al.,
2018). Several works done by ICARDA scientists and
students on MAS were published. (Halewood et al., 2018)
discriminates between resistant and susceptible chickpea
genotypes using two codominant markers associated
to Ascochyta blight. Molecular marker associated with
grain yield under drought conditions such as the CID,
are actively and eectively used in the ongoing breeding
program (Nachit, 1998; Nachit and Eloua, 2004). Dura
et al. (2012) identied potential targets for MAS of grain
yield improvement in durum wheat in ICARDA labora-
tory. Recently, the markers assisted selection has been
successfully used to enhance tolerance against Barley
scald (Sayed & Baum, 2018). Nowadays, ICARDA is fo-
cusing on Association mapping (AM) using phenotypic
and genotypic data of association panels, due to the im-
portance of this approach in identifying molecular mark-
ers (QTLs) linked to traits of interest for potential use in
marker assisted selection. In barley, association mapping
was undertaken to identify QTL eective against Psh in-
dividual races at seedling stage and QTL for quantitative
resistance to barley stripe rust at seedling and adult plant
stages (Visioni et al., 2018). In wheat, genome-wide as-
sociation mapping (GWAM) was employed using DArT
markers technology and ICARDA’s elite wheat genotypes
to identify markers linked to stripe rust resistance genes
in wheat for possible use in MAS (Tadesse et al., 2014;
Jighly et al., 2015) employed genome-wide association
mapping (GWAM). In pulse, the association mapping
was designed to determine the genetic basis of seed Fe
and Zn concentration in lentil by using single-nucleotide
polymorphism (SNP) array derived from cultivated lentil
sequences (Singh et al., 2017).
Successful application of MAS in last decade
e MAS of smart breeding method is the method of
choice for all breeders. It has been implemented in dif-
ferent crop programs. Several publications declare the
application of MAS in crop improvement. But still the
number of successful application of this method is less
compared to the number of QTLs mapped or markers
developed. Moreover, most marker associations are not
robust enough for successful marker assisted selection
(Young et al., 1999). By using Harzing’s Publsih or Perish
soware (Harzing, 2007) and using the query ‘Marker as-
sisted selection’ in Google scholar and in Scopus between
2010 and 2019, around 571 publications were retrieved
in which the title included ‘Marker assisted’. At rst sight
it was oen dicult to distinguish from the title whether
a publication is actually reporting a MAS application or
if only potential MAS applications of the actual research
outputs are discussed. erefore, the publications were

241
Mor. J. Agri. Sci. 1(5): 237-247, September 2020
selected by reading the abstracts and sometime the ma-
terial and methods to distinguish the real application of
MAS. e results mentioned in table 1 is the number
of publications harvested using MAS keyword. Among
571, only 189 publications were the real applications
of MAS. Whereas, others publications were reviews of
MAS (163 publications), QTL mapping or identica-
tion and/or marker development and validation (149
publications), Characterization and genetic diversity
(47 publications) or genomic selection (23 publications).
e MAS practical publications were dominant in rice
with 87 publications (Figure 1), 29 of them are on bacte-
rial blight diseases. e number of publications in other
cereals was limited to 39 publications (18 publications
in wheat, 18 publications in Maize and 3 publications in
Barley). Marker assisted backcrossing (100 publications,
Figure 2) has been most widely and successfully used
up-to-date, compared to other methods such as pedigree
method (40 publications), pyramiding (45 publications)
and MARS (4 publications). It has been applied to dif-
ferent crops, e.g. rice, wheat, maize, barley, pear millet,
soybean, tomato, etc.
Table 1: Total number and type of publications re-
trieved from Harzing’s Publish from 2010 to 2019
(Harzing, 2007)
Type of publications Number of publica-
tions
MAS articles 189
Reviews 163
Characterization or genetic diversity 47
Mapping or marker development 149
Genomic selection 23
Tota l 571
Figure 1: Number of MAS publications applied to dierent crops in the last 10 years
Figure 2: Number of publications for dierent types of MAS collected during the last 10 years

242 Henkrar and Udupa: Marker assisted selection for plant breeding
Table 2: Examples of successful use of marker assisted selection in dierent crops for the last 5 years
Target trait Gene (s)/QTl(s)
Type of
Marker
used
Name of marker used Crop Reference
Blast Pi2 STS Pi2–4 , HC28 Rice Yang et al., 2019
Bacterial blight and
aroma
Xa21, xa13, xa5,
fgr STS pTA248, RG136, RG556, BAD2 Rice Baliyan et al.,
2018
Quality protein Opaque2 (o2) SSR umc1066 and phi057 Maize
Hossain et al.,
2018; Pukalen-
thy et al., 2019
Scald Rrs1 SSR/
SCAR
Ebmac0871-
SSR, HVS3-SCAR, Bmag0006-
SSR
Barley Sayed and
Baum, 2018
HMW, Grain hardness,
Lipoxygenase,Yellow
pigment content,
Polyphenol oxidase,
Powdery mildew, Yel-
low rust, Pre-harvest
sprouting
SSR/STS,
allele
specic
UMN19, Bx7, ZSBy8, ZSBy9a,
UMN25, Dx5, UMN26, Pinb-D1a,
LOX16, LOX18, YP7A, YP7B-1,
YP7D-1, PPO18, PPO19, PPO29,
Pm2, Pm4b, Pm8, Xgwm582,
Xcfa2040, PHS1, PHS-4AL
Wheat Li et al., 2018
Blast Pi54, Pi1 and Pita STS, SSR Pi54MAS, RM224, YL155/87 Rice Khan et al., 2018
Bacterial blight Xa38, Xa21,
Xa13 and Xa5
Gene
specic
markers/
STS
Os04g53050-1, pTA248, xa13-
Prom, 10603-T10Dw Rice Yugander et al.,
2018
Bacterial blight Gm1, Gm4,
xa13 and Xa21 SSR RM1328, RM22550, xa13 prom and
pTA248 Rice
Krishnakumar
and Kumaravadi-
vel 2018
Mosaic virus RSC4, RSC8, and
RSC14Q SSR
BARCSOYSSR_14_1413, 4
BARCSOYSSR_14_1417,
BARCSOYSSR_14_1418,
BARCSOYSSR_02_0606,
BARCSOYSSR_02_0610, BARC-
SOYSSR_02_0616, BARCSOYS-
SR_02_0618, Satt334, Sct_033,
MY750
Soybean Wang et al.,
2017
Rust and coee berry SH3, SH?, Ck-1 SCAR/
SSR
SP-M16-SH3, BA-124-12K-f,
Sat244, BA-48-21OR, CaRHvII 2,
CaRHvII 3, CaRHvII 5, Sat 207,
Sat 235
Coea Alkimim et al.,
2017
Striga SG1, SG3, and
SG5 SSR 61RM2, SSR-1 and C42-2B Cowpea OMOIGUI et al.,
2017
Rust Lr19 and Lr24 SCAR/
SSR Xwmc221 and SCS1302 Wheat Singh et al.,
2017
Rust Lr24 and Lr28 SCAR/
SSR
SCS719, SCS1302607, SCS421570
and Xwmc313 Wheat Kumar et al.,
2017
Drought, Striga her-
monthica SNPs KASP
markers 233 SNPs with KASP assay Maize Abdulmalik et
al., 2017
Bacterial blight, Blast Xa21 and xa13,
Pi54 STS xa13 prom, pTA 248 and Pi54 MAS Rice Arunakumari et
al., 2016
Quality protein opaque2 SSR phi057 and umc1066 Maize Kostadinovic et
al., 2016
Grain protein con-
tent, Thousand grain
weight
GPC-B1 and TGW SSR Xucw108, Xgwm297 Wheat
Vishwakarma
et al., 2016 and
2014
Fusarium head blight Fhb7, Fhb1 SSR XsdauK66 and Xcfa2240 (Fhb7),
Xgwm493 and Xgwm533 (Fhb1) Wheat Guo et al. 2015
Leaf curl disease Ty-2, Ty-3, Ty-5 Linked
markers Ty-2, Ty-3, Ty-5, qTy10.1 Tomato Prasanna et al.,
2015
Blast and bacterial
blight Pi9(t), Xa23, tms5
SCAR/
EST/Indel
marker
Pb8, C189, IDtms5 Rice Ni et al., 2015
Rice tungro disease RTSV SSR RM336 Rice Shim et al., 2015

243
Mor. J. Agri. Sci. 1(5): 237-247, September 2020
e successful publications using MAS in last 5 years is
resumed in table 2. One successful example of marker
assisted backcrossing and pyramiding is the introgres-
sion of three BB resistance genes (Xa21, xa13 and xa5)
from BB-resistant donor variety IRBB-60 into the BB-
susceptible Basmati variety CSR-30 (Baliyan et al., 2018).
A successful introgression of Shoot Fly (Atherigona soc-
cataL. Moench) Resistance QTLs into Elite Post-rainy
Season Sorghum varieties (Gorthy et al., 2017). An
example of a successful application of MAS in breed-
ing new cultivars is the development of “Mura Salad”
a new fresh pepper cultivar (Capsicum annuum) con-
taining capsinoids, low-pungent capsaicinoid analogs
using dCAPs and SCAR markers (Tanaka et al., 2014).
In legumes, a successful application of marker assisted
backcrossing in chickpea and specic markers for Fu-
sarium wilt-resistance generate the development of new
cultivars Super Annigeri 1 and improved JG 74 with
enhanced resistance and improved yielding (Mannur
et al., 2019). MAS were successfully applied in wheat to
improve GPC-B1 (84-60) and also in Barley to transfer
a thermostable β-amylase gene (Xu et al., 2018) scald
(Rhynchosporium commune L.) resistance gene (Sayed
and Baum, 2018).
CONCLUSION
Marker assisted selection is a technology that has already
proved its value. Due to the number of QTLs, genes and
markers identied the MAS is likely to become more
valuable. Many organizations and private sectors suc-
ceed in implementing MAS and produced new lines
with desirable traits. But still reduced cost and optimized
strategies for integrating MAS with phenotypic selec-
tion are needed before the technology can reach its full
potential.
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